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RangeAugment: Efficient Online Augmentation with Range Learning


Dec 20, 2022
Sachin Mehta, Saeid Naderiparizi, Fartash Faghri, Maxwell Horton, Lailin Chen, Ali Farhadi, Oncel Tuzel, Mohammad Rastegari

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* Technical report (22 pages including references and appendix) 

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APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations


Oct 08, 2022
Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari, Oncel Tuzel, Fartash Faghri

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MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks


Jul 16, 2022
Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski

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Training Efficiency and Robustness in Deep Learning


Dec 02, 2021
Fartash Faghri

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* A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy 

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NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization


May 01, 2021
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy

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* This entry is redundant and was created in error. See arXiv:1908.06077 for the latest version 

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Bridging the Gap Between Adversarial Robustness and Optimization Bias


Feb 17, 2021
Fartash Faghri, Cristina Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux

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Adaptive Gradient Quantization for Data-Parallel SGD


Oct 23, 2020
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, Ali Ramezani-Kebrya

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* Accepted at the conference on Neural Information Processing Systems (NeurIPS 2020) 

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A Study of Gradient Variance in Deep Learning


Jul 09, 2020
Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba

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Adversarial Robustness through Regularization: A Second-Order Approach


Apr 04, 2020
Avery Ma, Fartash Faghri, Amir-massoud Farahmand

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